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Today I would like to recommend a super easy-to-use built-in function in Python, which is the lambda method. This tutorial will share with you roughly:
In Python, we often use the lambda keyword to declare an anonymous function, the so-called anonymous function, In layman's terms, it is a function without a name. The specific syntax format is as follows:
lambda arguments : expression
It can accept any number of parameters, but is only allowed to contain one expression, and the result of the expression is For the return value of the function, we can simply write an example:
(lambda x:x**2)(5)
output:
25
So how do we filter the elements in the list? Woolen cloth? Here you need to combine the lambda function and the filter() method, and the syntax format of the filter() method:
filter(function, iterable)
We have such a list:
import numpy as np yourlist = list(np.arange(2,50,3))
We want to filter out the elements that are less than 100 after the second power, let’s define an anonymous Function, as follows:
lambda x:x**2<100
The final result is as follows:
list(filter(lambda x:x**2<100, yourlist))
output:
[2, 5, 8]
If you encounter a complicated calculation process, the editor still recommends that you do it yourself Customize a function, but if it is a simple calculation process, lambda anonymous function is definitely the best choice.
The syntax of the map() function is similar to the filter() function above, such as the following anonymous function:
lambda x: x**2+x**3
We will It is used in conjunction with the map() method:
list(map(lambda x: x**2+x**3, yourlist))
output:
[12, 150, 576, 1452, 2940, 5202, ......]
Of course, as we mentioned before, the lambda anonymous function can accept multiple numbers of parameters, we can try it here For example, there are two sets of lists,
mylist = list(np.arange(4,52,3)) yourlist = list(np.arange(2,50,3))
We also use the map() method to operate, the code is as follows:
list(map(lambda x,y: x**2+y**2, yourlist,mylist))
output:
[20, 74, 164, 290, 452, 650, 884, 1154, ......]
apply() method is often used in Pandas data tables, and the lambda anonymous function is brought into the apply() method. We create a new data table as follows:
myseries = pd.Series(mylist) myseries
output:
04 17 2 10 3 13 4 16 5 19 6 22 7 25 8 28 ...... dtype: int32
The use of the apply() method is slightly different from the previous two. For both the map() method and the filter() method, we need to put the iterable object into it, and here The apply() does not need:
myseries.apply(lambda x: (x+5)/x**2)
output:
0 0.562500 1 0.244898 2 0.150000 3 0.106509 4 0.082031 5 0.066482 6 0.055785 7 0.048000 ...... dtype: float64
And if you encounter DataFarme table data, the same operation is done
df = pd.read_csv(r'Dummy_Sales_Data_v1.csv') df["Sales_Manager"] = df["Sales_Manager"].apply(lambda x: x.upper()) df["Sales_Manager"].head()
output:
0PABLO 1PABLO 2KRISTEN 3ABDUL 4 STELLA Name: Sales_Manager, dtype: object
And processing it through the apply() method is faster than processing it directly with the str.upper() method! !
So what are the scenarios that are not suitable for use? So first of all, the lambda function, as an anonymous function, is not suitable for assigning it to a variable, such as the following case:
squared_sum = lambda x,y: x**2 + y**2 squared_sum(3,4)
In comparison, it is better to customize a function for processing:
def squared_sum(x,y): return x**2 + y**2 squared_sum(3,4)
output:
25
When we encounter the following situation, we can simplify the code slightly:
import math mylist = [10, 25, 40, 49, 65, 81] sqrt_list = list(map(lambda x: math.sqrt(x), mylist)) sqrt_list
output:
[3.16227766, 5.0, 6.324555320, 7.0, 8.062257748, 9.0]
We can Simplified to:
import math mylist = [10, 25, 40, 49, 65, 81] sqrt_list = list(map(math.sqrt, mylist)) sqrt_list
output:
[3.162277, 5.0, 6.324555, 7.0, 8.062257, 9.0]
If it is a built-in function in Python, especially a module used for arithmetic such as math, it does not need to be placed in the lambda function, it can be directly Pull it out and use
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